{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google Bigtable\n",
    "\n",
    "> [Bigtable](https://cloud.google.com/bigtable) is a key-value and wide-column store, ideal for fast access to structured, semi-structured, or unstructured data. Extend your database application to build AI-powered experiences leveraging Bigtable's Langchain integrations.\n",
    "\n",
    "This notebook goes over how to use [Bigtable](https://cloud.google.com/bigtable) to store chat message history with the `BigtableChatMessageHistory` class.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/googleapis/langchain-google-bigtable-python/blob/main/docs/chat_message_history.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before You Begin\n",
    "\n",
    "To run this notebook, you will need to do the following:\n",
    "\n",
    "* [Create a Google Cloud Project](https://developers.google.com/workspace/guides/create-project)\n",
    "* [Create a Bigtable instance](https://cloud.google.com/bigtable/docs/creating-instance)\n",
    "* [Create a Bigtable table](https://cloud.google.com/bigtable/docs/managing-tables)\n",
    "* [Create Bigtable access credentials](https://developers.google.com/workspace/guides/create-credentials)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🦜🔗 Library Installation\n",
    "\n",
    "The integration lives in its own `langchain-google-bigtable` package, so we need to install it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -upgrade --quiet langchain-google-bigtable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Colab only**: Uncomment the following cell to restart the kernel or use the button to restart the kernel. For Vertex AI Workbench you can restart the terminal using the button on top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Automatically restart kernel after installs so that your environment can access the new packages\n",
    "# import IPython\n",
    "\n",
    "# app = IPython.Application.instance()\n",
    "# app.kernel.do_shutdown(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ☁ Set Your Google Cloud Project\n",
    "Set your Google Cloud project so that you can leverage Google Cloud resources within this notebook.\n",
    "\n",
    "If you don't know your project ID, try the following:\n",
    "\n",
    "* Run `gcloud config list`.\n",
    "* Run `gcloud projects list`.\n",
    "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.\n",
    "\n",
    "PROJECT_ID = \"my-project-id\"  # @param {type:\"string\"}\n",
    "\n",
    "# Set the project id\n",
    "!gcloud config set project {PROJECT_ID}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🔐 Authentication\n",
    "\n",
    "Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
    "\n",
    "- If you are using Colab to run this notebook, use the cell below and continue.\n",
    "- If you are using Vertex AI Workbench, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.colab import auth\n",
    "\n",
    "auth.authenticate_user()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Bigtable schema\n",
    "\n",
    "The schema for BigtableChatMessageHistory requires the instance and table to exist, and have a column family called `langchain`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @markdown Please specify an instance and a table for demo purpose.\n",
    "INSTANCE_ID = \"my_instance\"  # @param {type:\"string\"}\n",
    "TABLE_ID = \"my_table\"  # @param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the table or the column family do not exist, you can use the following function to create them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.cloud import bigtable\n",
    "from langchain_google_bigtable import create_chat_history_table\n",
    "\n",
    "create_chat_history_table(\n",
    "    instance_id=INSTANCE_ID,\n",
    "    table_id=TABLE_ID,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BigtableChatMessageHistory\n",
    "\n",
    "To initialize the `BigtableChatMessageHistory` class you need to provide only 3 things:\n",
    "\n",
    "1. `instance_id` - The Bigtable instance to use for chat message history.\n",
    "1. `table_id` : The Bigtable table to store the chat message history.\n",
    "1. `session_id` - A unique identifier string that specifies an id for the session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_google_bigtable import BigtableChatMessageHistory\n",
    "\n",
    "message_history = BigtableChatMessageHistory(\n",
    "    instance_id=INSTANCE_ID,\n",
    "    table_id=TABLE_ID,\n",
    "    session_id=\"user-session-id\",\n",
    ")\n",
    "\n",
    "message_history.add_user_message(\"hi!\")\n",
    "message_history.add_ai_message(\"whats up?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "message_history.messages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning up\n",
    "\n",
    "When the history of a specific session is obsolete and can be deleted, it can be done the following way.\n",
    "\n",
    "**Note:** Once deleted, the data is no longer stored in Bigtable and is gone forever."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "message_history.clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom client\n",
    "The client created by default is the default client, using only admin=True option. To use a non-default, a [custom client](https://cloud.google.com/python/docs/reference/bigtable/latest/client#class-googlecloudbigtableclientclientprojectnone-credentialsnone-readonlyfalse-adminfalse-clientinfonone-clientoptionsnone-adminclientoptionsnone-channelnone) can be passed to the constructor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.cloud import bigtable\n",
    "\n",
    "client = (bigtable.Client(...),)\n",
    "\n",
    "create_chat_history_table(\n",
    "    instance_id=\"my-instance\",\n",
    "    table_id=\"my-table\",\n",
    "    client=client,\n",
    ")\n",
    "\n",
    "custom_client_message_history = BigtableChatMessageHistory(\n",
    "    instance_id=\"my-instance\",\n",
    "    table_id=\"my-table\",\n",
    "    client=client,\n",
    ")"
   ]
  }
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